Neural networks are currently used in various fields. Typical feed-forward neural networks include a plurality of layers. In an example of the typical feed-forward neural networks, each layer receives the outputs of the previous layer. In each layer, a plurality of units having different weight parameters and bias parameters each produce a weighted linear sum of the outputs of the previous layer and output the weighted linear sum to the next layer through an active function.
Although the neural network is a very strong recognizer, it is better for further improving the recognition performance to adapt the neural network to the recognition target. For example, a technique has been developed to correct the difference among the classes and perform task adaptation by adding a correction term, for each class, to the bias parameter on the linear sum in the units in each layer of the neural network. Another technique has been developed to perform the task adaptation by copying a feature vector x in the number of classes as features for the recognizer. Another technique has been developed to adapt the neural network to the classes by using a feature of (x, x, 0) for learning and evaluating a class A and using a feature of (x, 0, x) for learning and evaluating a class B, for example.